RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

Resumé

Two algorithms, RecurSIA and RRT,are presented,designed to increase the compression factor achieved using SIATEC-based point-set cover algorithms. RecurSIA recursively applies a TEC cover algorithm to the patterns intheTECs that it discovers. RRT attempts to remove translators from each TEC without reducing its covered set. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.
OriginalsprogEngelsk
Titel12th International Workshop on Machine Learning and Music (MML 2019)
Antal sider6
ForlagSpringer
StatusAccepteret/In press - 2020
BegivenhedInternational Workshop on Machine Learning and Music - Würzburg, Tyskland
Varighed: 16 sep. 201916 sep. 2019
Konferencens nummer: 12
https://musml2019.weebly.com/

Konference

KonferenceInternational Workshop on Machine Learning and Music
Nummer12
LandTyskland
ByWürzburg
Periode16/09/201916/09/2019
Internetadresse

Citer dette

Meredith, D. (Accepteret/In press). RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. I 12th International Workshop on Machine Learning and Music (MML 2019) Springer.
Meredith, David. / RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. 12th International Workshop on Machine Learning and Music (MML 2019). Springer, 2020.
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title = "RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators",
abstract = "Two algorithms, RecurSIA and RRT,are presented,designed to increase the compression factor achieved using SIATEC-based point-set cover algorithms. RecurSIA recursively applies a TEC cover algorithm to the patterns intheTECs that it discovers. RRT attempts to remove translators from each TEC without reducing its covered set. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.",
keywords = "machine learning, music analysis, data compression, point-set patterns, pattern discovery, minimum description length, algorithms",
author = "David Meredith",
year = "2020",
language = "English",
booktitle = "12th International Workshop on Machine Learning and Music (MML 2019)",
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Meredith, D 2020, RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. i 12th International Workshop on Machine Learning and Music (MML 2019). Springer, International Workshop on Machine Learning and Music, Würzburg, Tyskland, 16/09/2019.

RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. / Meredith, David.

12th International Workshop on Machine Learning and Music (MML 2019). Springer, 2020.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

TY - GEN

T1 - RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators

AU - Meredith, David

PY - 2020

Y1 - 2020

N2 - Two algorithms, RecurSIA and RRT,are presented,designed to increase the compression factor achieved using SIATEC-based point-set cover algorithms. RecurSIA recursively applies a TEC cover algorithm to the patterns intheTECs that it discovers. RRT attempts to remove translators from each TEC without reducing its covered set. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.

AB - Two algorithms, RecurSIA and RRT,are presented,designed to increase the compression factor achieved using SIATEC-based point-set cover algorithms. RecurSIA recursively applies a TEC cover algorithm to the patterns intheTECs that it discovers. RRT attempts to remove translators from each TEC without reducing its covered set. When evaluated with COSIATEC, SIATECCompress and Forth’s algorithm on the JKU Patterns Development Database, using RecurSIA with or without RRT increased compression factor and recall but reduced precision. Using RRT alone increased compression factor and reduced recall and precision, but had a smaller effect than RecurSIA.

KW - machine learning

KW - music analysis

KW - data compression

KW - point-set patterns

KW - pattern discovery

KW - minimum description length

KW - algorithms

UR - https://arxiv.org/abs/1906.12286

UR - https://github.com/chromamorph/omnisia-recursia-rrt-mml-2019

M3 - Article in proceeding

BT - 12th International Workshop on Machine Learning and Music (MML 2019)

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Meredith D. RECURSIA-RRT: Recursive translatable point-set pattern discovery with removal of redundant translators. I 12th International Workshop on Machine Learning and Music (MML 2019). Springer. 2020